library(MASS)     # Datasets
library(mice)     # Boys dataset
library(dplyr)    # Data manipulation
library(magrittr) # Pipes
library(ggplot2)  # Plotting suite
library(sf)       # Spatial features

New functions:

XXXXX

• plot(): R’s plotting device
• barplot(): bar plot function
• pie(): pie plot function
• hist(): histogram
• density(): function that calculates the density
• boxplot(): box plot function XXXXX

• We can process a lot of information quickly with our eyes
• Plots give us information about
  • Distribution / shape
  • Irregularities
  • Assumptions
  • Intuitions
• Summary statistics, correlations, parameters, model tests, p-values do not tell the whole story

Source: Anscombe, F. J. (1973). “Graphs in Statistical Analysis”. American Statistician. 27 (1): 17–21.

Source: https://www.autodeskresearch.com/publications/samestats

• A few plots in base graphics in R
• Plotting with ggplot2 graphics
• Plotting data on maps

• Vectors

height <- c(50.1, 53.5, 50.0, 54.5, 57.5)
weight <- c(3.65, 3.37, 3.14, 4.27, 5.03)

• Data frames

boys <- boys
head(boys)

##      age  hgt   wgt   bmi   hc  gen  phb tv   reg
## 3  0.035 50.1 3.650 14.54 33.7 <NA> <NA> NA south
## 4  0.038 53.5 3.370 11.77 35.0 <NA> <NA> NA south
## 18 0.057 50.0 3.140 12.56 35.2 <NA> <NA> NA south
## 23 0.060 54.5 4.270 14.37 36.7 <NA> <NA> NA south
## 28 0.062 57.5 5.030 15.21 37.3 <NA> <NA> NA south
## 36 0.068 55.5 4.655 15.11 37.0 <NA> <NA> NA south

To subset a vector use square brackets v[1:5]

To call a variable in the data frame, use the $ notation:

boys$hgt

boys$hgt[1:10]

##  [1] 50.1 53.5 50.0 54.5 57.5 55.5 52.5 53.0 55.1 54.5

plot(x = boys$hgt, y = boys$wgt, main = "Scatter plot", 
     xlab = "Height", ylab = "Weight", bty = "L")

Breaking this down:

plot( # Calling the base plot function
      x = boys$hgt, # x coordinates
      y = boys$wgt, # y coordinates
      main = "Scatter plot",# Plot title
      xlab = "Height", # x-axis label
      ylab = "Weight", # y axis label
      bty = "L" # Type of box around plot: "Shaped like an L"
      )

See ?par for more graphical parameters

Two ways of doing the same thing

plot(x = boys$hgt, y = boys$wgt) 
plot( wgt ~ hgt, data = boys)

The latter is the plot method for formulas. (Formulas are an R class!)

plot(x = 1:5, y = exp(1:5), type = "l", main = "Line chart", bty = "L")

Beyond basic scatter/line plots, there are specific functions:

counts <- table(boys$reg)

barplot(counts, main="Bar chart", ylab = "N")

counts <- table(boys$reg)

pie(x=counts, main="Pie chart")

Let’s go one step back:

counts <- table(boys$reg)

counts

## 
## north  east  west south  city 
##    81   161   239   191    73

hist(boys$hgt, main = "Histogram", xlab = "Height")

boxplot(boys$hgt ~ boys$reg, main = "Box plot", 
        xlab = "Region", ylab = "Height")

• plot(): Generic function for plotting of R objects
• barplot(): bar plots
  
• pie(): pie charts
• hist(): histograms
• boxplot(): box plots function
• par(): Set or query graphical parameters
• density(): function that calculates the density

• If we want to be able to set visual standards easily
• … and have a more unified graphics system
• … at the cost of some abstraction and a steeper intial learning curve
• we go ggplot

Layered plotting based on the book The Grammar of Graphics by Leland Wilkinsons.

With ggplot2 you

1. provide the data
2. define how to map variables to aesthetics
3. state which geometric object to display
4. (optional) edit the overall theme of the plot

ggplot2 then takes care of the details

1: Provide the data

ggplot( data = boys )

2: map variable to aesthetics

  ggplot( data = boys, aes(x = hgt, y = wgt) )

3: state which geometric object to display

ggplot( data = boys, aes(x = hgt, y = wgt) ) +
  geom_point()

Create the plot

gg <- 
  ggplot( data = boys, aes( x = hgt, y = wgt ) ) +
  geom_point( col = "dark green" )

Add another layer (smooth fit line)

gg <- gg + 
  geom_smooth(col = "dark blue")

Give it some labels and a nice look

gg <- gg + 
  labs(x = "Age", y = "BMI", title = "BMI trend for boys") +
  theme_minimal()

plot(gg)

• x
• y
• size
• colour
• fill
• opacity (alpha)
• linetype
• …

gg <- 
  boys %>% 
  filter(!is.na(reg)) %>% 
  
  ggplot(aes(x      = hgt, 
             y      = wgt, 
             shape  = reg, 
             colour = age)) +
  
  geom_point( alpha = 0.5) +
  
  labs(title  = "Trend for boys",
       x      = "Height", 
       y      = "Weight", 
       shape    = "Region",
       colour = "Age") +
  theme_minimal()

plot(gg)

• geom_point

• geom_bar

• geom_line

• geom_smooth

• geom_histogram

• geom_boxplot

• geom_density

facet_wrap() and facet_grid() divide figures into panels.

boys %>%
  ggplot(aes(x = age, y = bmi)) +
  geom_point() +
  geom_smooth() + 
  facet_wrap(~ reg)

Easy with ggsave()

# save as pdf
ggssave("plot.pdf", myplot)

# save as png and specify dimensions 
ggssave("plot.png", myplot, width = 7, height = 5, units="in")

• A formal standard (ISO 19125-1:2004) that describes how objects in the real world can be represented in computers, with emphasis on the spatial geometry of these objects.
• As implemented e.g. in ArcGIS
• Implemented for R in the sf package
• Feature geometries are stored in data.frames

We have time for a cursory introduction at most.

denmark <- st_read("DK_map.shp")

plot(st_geometry(denmark))

denmark$proportion.over.70 <- denmark$over70/denmark$population

plot(denmark["proportion.over.70"],
     main = "Proportion of population aged 70 years and above")

denmark %>% ggplot(aes(fill=proportion.over.70)) + geom_sf()